NDLI: PM10and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

Content Provider	Springer Nature : BioMed Central
Author	Ielpo, Pierina Paolillo, Vincenzo de Gennaro, Gianluigi Dambruoso, Paolo Rosario
Abstract	Background The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality. In particular, the exposure to finer particles can cause short and long-term effects on human health. In the present paper PM10 (particulate matter with aerodynamic diameter lower than 10 μm), CO, NOx (NO and NO2), Benzene and Toluene trends monitored in six monitoring stations of Bari province are shown. The data set used was composed by bi-hourly means for all parameters (12 bi-hourly means per day for each parameter) and it’s referred to the period of time from January 2005 and May 2007. The main aim of the paper is to provide a clear illustration of how large data sets from monitoring stations can give information about the number and nature of the pollutant sources, and mainly to assess the contribution of the traffic source to PM10 concentration level by using multivariate statistical techniques such as Principal Component Analysis (PCA) and Absolute Principal Component Scores (APCS). Results Comparing the night and day mean concentrations (per day) for each parameter it has been pointed out that there is a different night and day behavior for some parameters such as CO, Benzene and Toluene than PM10. This suggests that CO, Benzene and Toluene concentrations are mainly connected with transport systems, whereas PM10 is mostly influenced by different factors. The statistical techniques identified three recurrent sources, associated with vehicular traffic and particulate transport, covering over 90% of variance. The contemporaneous analysis of gas and PM10 has allowed underlining the differences between the sources of these pollutants. Conclusions The analysis of the pollutant trends from large data set and the application of multivariate statistical techniques such as PCA and APCS can give useful information about air quality and pollutant’s sources. These knowledge can provide useful advices to environmental policies in order to reach the WHO recommended levels.
Related Links	https://bmcchem.biomedcentral.com/counter/pdf/10.1186/1752-153X-8-14.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/1752-153X-8-14
Journal	BMC Chemistry
Issue Number	1
Volume Number	8
Language	English
Publisher	BioMed Central
Publisher Date	2014-02-21
Access Restriction	Open
Subject Keyword	Chemistry Food Science PM10 Gaseous pollutants Air monitoring stations PCA APCS Source apportionment Chemistry/Food Science
Content Type	Text
Resource Type	Article
Subject	Chemistry
Journal Impact Factor	4.3/2023
5-Year Journal Impact Factor	4.4/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

Gaseous Pollutants and PM 10 Trends From Monitoring Networks in Bari Province: PCA and APCS Applications on a Two Years and Half Data Set

Source apportionment of ambient non-methane hydrocarbons in Hong Kong : Application of a principal component analysis / absolute principal component scores ( PCA / APCS ) receptor model

Source apportionment of water pollutants in Poyang Lake Basin in China using absolute principal component score–multiple linear regression model combined with land-use parameters

Source apportionment of groundwater pollutants in Apulian agricultural sites using multivariate statistical analyses: case study of Foggia province

Sources Affecting PM2.5 Concentrations at a Rural Semi-Arid Coastal Site in South Texas

Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses

Chemical characteristics and source apportionment of PM$_{2.5}$ using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India

Receptor modeling for source apportionment of polycyclic aromatic hydrocarbons in urban atmosphere

PM10and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

Similar Documents

PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

Gaseous Pollutants and PM 10 Trends From Monitoring Networks in Bari Province: PCA and APCS Applications on a Two Years and Half Data Set

Source apportionment of ambient non-methane hydrocarbons in Hong Kong : Application of a principal component analysis / absolute principal component scores ( PCA / APCS ) receptor model

Source apportionment of water pollutants in Poyang Lake Basin in China using absolute principal component score–multiple linear regression model combined with land-use parameters

Source apportionment of groundwater pollutants in Apulian agricultural sites using multivariate statistical analyses: case study of Foggia province

Sources Affecting PM2.5 Concentrations at a Rural Semi-Arid Coastal Site in South Texas

Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses

Chemical characteristics and source apportionment of PM$_{2.5}$ using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India

Receptor modeling for source apportionment of polycyclic aromatic hydrocarbons in urban atmosphere

PM10and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set